The Identification Of Certain Nonlinear Systems By Only Observing The Output

Rozario, N.; Papoulis, A.

doi:10.1109/hosa.1989.735274

Cited by 13 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So far, mostly input/output-based (I/O-based) system identification methods have been developed for nonlinear channels (see e.g., [29]), while the blind scenario has not been addressed in its generality. Only methods that assume that the channels and the input signals satisfy special (and often restrictive) conditions have been developed [26]- [28]. For example, the model adopted in [27] consists of two linear subsystems separated by a polynomial-type zero-memory nonlinearity (the LTI-ZMNL-LTI model), which represents a particular case of a Volterra filter with factorizable kernels.…”

Section: Introductionmentioning

confidence: 99%

Linear multichannel blind equalizers of nonlinear FIR Volterra channels

Giannakis

Serpedin

1997

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Truncated Volterra expansions model nonlinear systems encountered with satellite communications, magnetic recording channels, and physiological processes. A general approach for blind deconvolution of single-input multiple-output Volterra finite impulse response (FIR) systems is presented. It is shown that such nonlinear systems can be blindly equalized using only linear FIR filters. The approach requires that the Volterra kernels satisfy a certain coprimeness condition and that the input possesses a minimal persistence-of-excitation order. No other special conditions are imposed on the kernel transfer functions or on the input signal, which may be deterministic or random with unknown statistics. The proposed algorithms are corroborated with simulation examples.

show abstract

Section: Introductionmentioning

confidence: 99%